


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deta'
    __magic_name__         =						{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(			self     ,   __snake_case=None     ,   __snake_case=9_0_0     ,   __snake_case=2_0_4_8     ,   __snake_case=6     ,   __snake_case=2_0_4_8     ,   __snake_case=8     ,   __snake_case=6     ,   __snake_case=1_0_2_4     ,   __snake_case=8     ,   __snake_case=0.0     ,   __snake_case=True     ,   __snake_case="relu"     ,   __snake_case=2_5_6     ,   __snake_case=0.1     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1.0     ,   __snake_case=True     ,   __snake_case=False     ,   __snake_case="sine"     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=4     ,   __snake_case=True     ,   __snake_case=3_0_0     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=1     ,   __snake_case=5     ,   __snake_case=2     ,   __snake_case=1     ,   __snake_case=1     ,   __snake_case=5     ,   __snake_case=2     ,   __snake_case=0.1     ,   __snake_case=0.25     ,   **__snake_case     ,   ):
         if backbone_config is None:
              logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
              snake_case											=							CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']       )
         else:
              if isinstance(__snake_case     ,   __snake_case       ):
                   snake_case											=							backbone_config.pop('''model_type'''       )
                   snake_case											=							CONFIG_MAPPING[backbone_model_type]
                   snake_case											=							config_class.from_dict(__snake_case       )

         snake_case											=							backbone_config
         snake_case											=							num_queries
         snake_case											=							max_position_embeddings
         snake_case											=							d_model
         snake_case											=							encoder_ffn_dim
         snake_case											=							encoder_layers
         snake_case											=							encoder_attention_heads
         snake_case											=							decoder_ffn_dim
         snake_case											=							decoder_layers
         snake_case											=							decoder_attention_heads
         snake_case											=							dropout
         snake_case											=							attention_dropout
         snake_case											=							activation_dropout
         snake_case											=							activation_function
         snake_case											=							init_std
         snake_case											=							init_xavier_std
         snake_case											=							encoder_layerdrop
         snake_case											=							auxiliary_loss
         snake_case											=							position_embedding_type
         # deformable attributes
         snake_case											=							num_feature_levels
         snake_case											=							encoder_n_points
         snake_case											=							decoder_n_points
         snake_case											=							two_stage
         snake_case											=							two_stage_num_proposals
         snake_case											=							with_box_refine
         snake_case											=							assign_first_stage
         if two_stage is True and with_box_refine is False:
              raise ValueError('''If two_stage is True, with_box_refine must be True.'''       )
         # Hungarian matcher
         snake_case											=							class_cost
         snake_case											=							bbox_cost
         snake_case											=							giou_cost
         # Loss coefficients
         snake_case											=							mask_loss_coefficient
         snake_case											=							dice_loss_coefficient
         snake_case											=							bbox_loss_coefficient
         snake_case											=							giou_loss_coefficient
         snake_case											=							eos_coefficient
         snake_case											=							focal_alpha
         super().__init__(is_encoder_decoder=__snake_case     ,   **__snake_case       )
    @property
    def 						a_     (			self       ):
         return self.encoder_attention_heads
    @property
    def 						a_     (			self       ):
         return self.d_model




    def 						a_     (			self       ):
         snake_case											=							copy.deepcopy(self.__dict__       )
         snake_case											=							self.backbone_config.to_dict()
         snake_case											=							self.__class__.model_type
         return output








def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




